Hardware Centric Machine Vision for High Precision Center of Gravity Calculation
We present a hardware oriented method for real-time
measurements of object-s position in video. The targeted application
area is light spots used as references for robotic navigation. Different
algorithms for dynamic thresholding are explored in combination
with component labeling and Center Of Gravity (COG) for highest
possible precision versus Signal-to-Noise Ratio (SNR). This method
was developed with a low hardware cost in focus having only one
convolution operation required for preprocessing of data.
[1] Larsson U., Zell C., Hyypp├ñ K., Wernersson Å.: Navigating an
Articulated Vehicle and Reversing with a Trailer. Proceedings 1994
IEEE International Conference on Robotics and Automation, vol. 3, pp.
2398--2404, San Diego, USA (1994).
[2] Wolf W., Ozer C., Lv T.: Smart cameras as embedded systems.
Computer, vol. 35, no. 9 (2002).
[3] Dias F., Berry F., Serot J., Marmoiton F.: Hardware, design and
implementation issues on a fpga-based smart camera. Proc. First
ACM/IEEE international conference on distributed smart cameras. pp
20--26, Vienna, Austria (2007).
[4] Carsten Steger, Markus Ulrich and Christian Wiedemann,Machine
vision algorithms and applications, Wiley-VCH 2008.
[5] Wnuk M.: Remarks on hardware implementation of image processing
algorithms. Int. journal of applied mathematics and computer science.
Vol. 18, No. 1, pp105--110 (2008).
[6] H. C. van Assen, M. Egmont-Petersen, and J. H. C. Reiber, "Accurate
Object Localization in Gray Level Images Using the Center of Gravity
Measure: Accuracy Versus Precision, IEEE Transaction on Image
Processing," Vol. 11, No.12 December 2002.
[7] R.C. Gonzales and R.E. Woods, Addison Wesley,Digital Image
Processing, 2008, third edition.
[8] A. Patwardhan, Subpixel position measurement using 1D,2D and 3D
centroid algorithms with emphasis on applications in confocal
microscopy, Journal of Microscopy, Vol. 186,Pt 3, June 1997, pp. 246-
257.
[9] Alexander Fish, Dmitry Akselrod and Orly Yadid-Pecht-Pecht, High
Precision Image Centroid Computation via an Adaptive K-Winner-
Take-all Circuit in Conjunction with a Dynamic Element Matching
Algorithm for Star Tracking Applications, Analog Integrated Circuits
and Signal Processing, 39, 251-266, 2004.
[10] G.A.W. West, & T.A. Clarke, 1990, "A survey and examination of
subpixel measurement techniques.", ISPRS Int. Conf. on Close Range
Photogrammetry and Machine Vision, SPIE Vol. 1395, pp 456 - 463,
Sept. 3-7.
[11] Clarke, T.A. Cooper, M.A.R. & Fryer, J.G., 1993. An estimator for the
random error in subpixel target location and its use in the bundle
adjustment. Optical 3-D measurements techniques II, Pub. Wichmann,
Karlsruhe:161-168.
[12] B.Thörnberg et al. "Bit-Width Constrained Memory Hierarchy
Optimization for Real-Time Video Systems", IEEE Transactions on
Computer-Aided Design of Integrated Circuits and Systems, Vol26, No
4, pp 781-800, April 2007.
[13] B. Thörnberg and N. Lawal, "Real-time component labelling and feature
extraction on FPGA", Proc. of International Symposium on Signals,
Circuits and Systems, Iasi, Romania 2009,
[1] Larsson U., Zell C., Hyypp├ñ K., Wernersson Å.: Navigating an
Articulated Vehicle and Reversing with a Trailer. Proceedings 1994
IEEE International Conference on Robotics and Automation, vol. 3, pp.
2398--2404, San Diego, USA (1994).
[2] Wolf W., Ozer C., Lv T.: Smart cameras as embedded systems.
Computer, vol. 35, no. 9 (2002).
[3] Dias F., Berry F., Serot J., Marmoiton F.: Hardware, design and
implementation issues on a fpga-based smart camera. Proc. First
ACM/IEEE international conference on distributed smart cameras. pp
20--26, Vienna, Austria (2007).
[4] Carsten Steger, Markus Ulrich and Christian Wiedemann,Machine
vision algorithms and applications, Wiley-VCH 2008.
[5] Wnuk M.: Remarks on hardware implementation of image processing
algorithms. Int. journal of applied mathematics and computer science.
Vol. 18, No. 1, pp105--110 (2008).
[6] H. C. van Assen, M. Egmont-Petersen, and J. H. C. Reiber, "Accurate
Object Localization in Gray Level Images Using the Center of Gravity
Measure: Accuracy Versus Precision, IEEE Transaction on Image
Processing," Vol. 11, No.12 December 2002.
[7] R.C. Gonzales and R.E. Woods, Addison Wesley,Digital Image
Processing, 2008, third edition.
[8] A. Patwardhan, Subpixel position measurement using 1D,2D and 3D
centroid algorithms with emphasis on applications in confocal
microscopy, Journal of Microscopy, Vol. 186,Pt 3, June 1997, pp. 246-
257.
[9] Alexander Fish, Dmitry Akselrod and Orly Yadid-Pecht-Pecht, High
Precision Image Centroid Computation via an Adaptive K-Winner-
Take-all Circuit in Conjunction with a Dynamic Element Matching
Algorithm for Star Tracking Applications, Analog Integrated Circuits
and Signal Processing, 39, 251-266, 2004.
[10] G.A.W. West, & T.A. Clarke, 1990, "A survey and examination of
subpixel measurement techniques.", ISPRS Int. Conf. on Close Range
Photogrammetry and Machine Vision, SPIE Vol. 1395, pp 456 - 463,
Sept. 3-7.
[11] Clarke, T.A. Cooper, M.A.R. & Fryer, J.G., 1993. An estimator for the
random error in subpixel target location and its use in the bundle
adjustment. Optical 3-D measurements techniques II, Pub. Wichmann,
Karlsruhe:161-168.
[12] B.Thörnberg et al. "Bit-Width Constrained Memory Hierarchy
Optimization for Real-Time Video Systems", IEEE Transactions on
Computer-Aided Design of Integrated Circuits and Systems, Vol26, No
4, pp 781-800, April 2007.
[13] B. Thörnberg and N. Lawal, "Real-time component labelling and feature
extraction on FPGA", Proc. of International Symposium on Signals,
Circuits and Systems, Iasi, Romania 2009,
@article{"International Journal of Engineering, Mathematical and Physical Sciences:64561", author = "Xin Cheng and Benny Thörnberg and Abdul Waheed Malik and Najeem Lawal", title = "Hardware Centric Machine Vision for High Precision Center of Gravity Calculation", abstract = "We present a hardware oriented method for real-time
measurements of object-s position in video. The targeted application
area is light spots used as references for robotic navigation. Different
algorithms for dynamic thresholding are explored in combination
with component labeling and Center Of Gravity (COG) for highest
possible precision versus Signal-to-Noise Ratio (SNR). This method
was developed with a low hardware cost in focus having only one
convolution operation required for preprocessing of data.", keywords = "Dynamic thresholding, segmentation, position measurement, sub-pixel precision, center of gravity.", volume = "4", number = "4", pages = "493-8", }
